Phenotypic Effects of Polygenic Risk for Schizophrenia

Special Issue Information

In recent years, polygenic risk scores that summarize an individual’s polygenic risk for schizophrenia (SZ) have been increasingly researched, both in patient and control populations. This special issue of the Journal of Psychiatry and Brain Science aims to provide a clearer picture of the associations of phenotypes with SZ polygenic risk. Articles that will be published in this special issue are not limited to any specific clinical or non-clinical population or kind of phenotype (e.g. behavioral or biological), as it is our goal to capture the full spectrum of effects of SZ polygenic risk. Importantly, also articles with negative findings (non-associations) of phenotypes theoretically thought to be related to polygenic risk for SZ are explicitly invited.

We are welcoming both original research articles and review articles, and article processing fees for the papers submitted to this Special Issue are totally waived.

Planned Papers

1Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, Ludwig Maximilian University Munich

2Department of Psychiatry, University Hospital, Ludwig Maximilian University Munich

Abstract:A mental disorder is a multifactorial phenomenon in which not only environmental influences but also genetic factors play an important role. The basis for this is a complex mode of inheritance with high polygenicity, in which a large number of common genetic variants with small effects are decisive. One way to measure this polygenic risk is the calculation of polygenic risk scores (PRS). These reflect the complex multifactorial interaction of coding and regulatory DNA variants in the development of mental illness.

In addition to the genetic factors, environmental influences also play an important role in the development of mental illnesses. It is known that the use of cannabis in patients with schizophrenia (SCZ) and bipolar disorder (BD) is much higher than in the general population. Studies have shown that a genetic predisposition to schizophrenia is associated with increased cannabis use. Power et al. have used PRS analyses and found that healthy individuals with cannabis use have a higher PRS for schizophrenia than individuals who do not use cannabis (R2 = 0.47%,p = 2.6 × 10−4). Verweij et al. were able to achieve similar results: Individuals with increased PRS for schizophrenia consume more cannabis throughout their lives than individuals with a lower risk score (R2 = 3.3% p < 0.001). In view of this relationship, we also investigated whether PRS for schizophrenia can also predict cannabis use in patients with schizophrenia and bipolar disorder. In addition, we tested whether cannabis-PRS has an influence on cannabis use in these two patient groups.

Although an exact clinical prognosis based on PRS is not possible at the present, the results found by PRS investigations so far are quite promising. Initial results suggest that people with SCZ or BD and an increased polygenic risk of schizophrenia are more likely to use cannabis. The connection between mental illnesses and cannabis use could therefore not simply be seen as an environmental risk, but rather explained as a gene-environment interaction. In the future, larger sample sizes will be necessary to investigate the genetic association between a mental disorder and cannabis use and to identify common genes and biological mechanisms that can explain this genetic association.

Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz

Abstract:Bipolar disorder is a mood disorder with mood swings between the pole of depression and the pole of mania. The pathomechanism of those mood swings has not been completely elucidated yet. According to current scientific knowledge affective episodes are caused by a concatenation between a genetic predisposition and biopsychosocial triggers. Even though diverse genome wide associated gene variants (e.g. single nucleotide polymorphisms of CACNA1C, ANK3 or NCAN) were associated with Bipolar disorder in the last decade, there is still missing heritability. Thus, innovative approaches and endophenotypes of Bipolar disorder must be used to decipher the underlying pathomechanisms of Bipolar disorder. Recently, the Polygenic Risk Score for Schizophrenia was investigated in the lithium-response endophenotype of Bipolar disorder. The polygenic score for Schizophrenia was inversely associated with the lithium response phenotype in study participants with Bipolar disorder. The latter underlines the genetic overlaps between psychiatric diseases, which also demonstrates the importance of cross-disorder design studies to elucidate the missing heritability of Bipolar disorder.

Keywords:Bipolar Disorder, Polygenic Risk Score, Schizophrenia

Type of Paper:Review

Paper Title:Let’s talk about the association between schizophrenia polygenic risk scores and cognition in patients and the general population: A review

Abstract:In the late 19th century, Emil Kraepelin divided endogeneous psychoses into manic-depressive psychosis and dementia praecox. The latter term described individuals with schizophrenia who also suffered from cognitive deficits. Studies have since consistently shown evidence of schizophrenia patients having lower cognitive performance. Not only do cognitive deficits precede the acute symptoms of psychosis and persist afterwards, impairments also often appear already during childhood. Twin studies revealed genetic effects on cognitive performance. Additionally, first degree relatives show lower cognitive performance than healthy controls, but better performance than patients, which makes cognition an interesting endophenotype for schizophrenia. During the last years, several studies have explored the relationship between common genetic variants associated with schizophrenia (in the form of schizophrenia polygenic risk scores) and cognition both in patient samples and the general population. This review gives an overview of current results and discusses possible clinical implications.

Abstract:Polygenic risk scores (PRSs) have been exploited to predict numerous complex phenotypes especially in psychiatry, e.g., psychosis. However, a far-reaching understanding of the properties of PRSs is critical to interpreting such analyses well. In a simulation study we investigate the properties of PRS analyses, mainly the distribution of explained variance (R2) and optimal p-value threshold across 100 replicates. We also apply this approach to the Global Assessment of Functioning (GAF) scores in a sample of patients from the psychotic-to-affective continuum (PsyCourse, N = 431), employing the PRS derived from the results of a large genome-wide association study (GWAS) of schizophrenia by the Psychiatric Genomics Consortium.

We simulated both discovery and target phenotypes using a quantitative additive genetic model. The discovery sample comprised a simulation of 50K markers for 34K individuals based on the European HapMap reference population CEU. Thereby a discovery trait was simulated with 80% additive genetic variance explained by 20 out of 50K markers. Implementing the same phenotype and genotype generation model as the discovery trait, we simulated target trait ‘T1’, in target samples of varying sizes (1000, 500, 200, and 100 individuals). We also generated two more target traits T2 and T3 with 80% and 60% correlation with T1, respectively.

The simulated data analyses for T1 across all sample sizes show a normally distributed R2 with mean value ranging from 30.6% to 33.7% across 100 replicates. For T2 mean R2 values range from 19.6% to 21.5%, and for T3 from 11.4% to 12.4%. Target traits with lower correlation to T1 and hence to the discovery trait also had lower mean R2, this holds true for all sample sizes. The distribution of optimal p-value thresholds for the target traits is shifted more to the left with smaller correlation with T1 and thus with the discovery trait. This means that fewer SNPs are integrated in the final PRS for the target trait with the best possible R2.

Next we used the PRS derived for schizophrenia by the PGC as a discovery trait, which is also based on 34K individuals The target trait is GAF, thus we will estimate with optimal p-value threshold how much of the variation in GAF is explained by this derived PRS in patients of the PsyCourse cohort. We will interpret these results in light of the simulations.